From david at drhagen.com  Wed Mar 11 09:16:13 2020
From: david at drhagen.com (David Hagen)
Date: Wed, 11 Mar 2020 09:16:13 -0400
Subject: [SciPy-User] Optimization methods for fitting ODEs
Message-ID: <CALO6Ft5-6WoazbM1sYRxkb-h4DydFKSPU4j3de3gOZxXsSgtbQ@mail.gmail.com>

TL;DR: Anyone have a recommended optimization method for fitting ODEs to
data when I have the analytic gradient?

I am currently fitting the numeric solutions of ODEs to data. I wrote an
objective function, a gradient on that objective function, and passed both
to scipy.optimize.minimize. This worked fine on small problems, but all the
scipy minimize methods broke down when the number of parameters got large
(~40). My past experience in Matlab suggests that this happens because some
parameter directions are MUCH more tightly constrained than others.

The scipy methods are easy-to-use, but I was wondering if there were any
alternatives that people had found to work well when the problem got too
hard for scipy. Here is a list of features that I probably need:

* black-box optimizer
* local optimizer
* uses supplied analytic gradient
* allows bounds on parameters
* accepts a maximum step size
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From ralf.gommers at gmail.com  Wed Mar 11 10:15:03 2020
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Wed, 11 Mar 2020 15:15:03 +0100
Subject: [SciPy-User] Optimization methods for fitting ODEs
In-Reply-To: <CALO6Ft5-6WoazbM1sYRxkb-h4DydFKSPU4j3de3gOZxXsSgtbQ@mail.gmail.com>
References: <CALO6Ft5-6WoazbM1sYRxkb-h4DydFKSPU4j3de3gOZxXsSgtbQ@mail.gmail.com>
Message-ID: <CABL7CQgL+oG7pQb5YWttij48KsmW2b9C-DCD2czNDCHAR-6ksw@mail.gmail.com>

On Wed, Mar 11, 2020 at 2:42 PM David Hagen <david at drhagen.com> wrote:

> TL;DR: Anyone have a recommended optimization method for fitting ODEs to
> data when I have the analytic gradient?
>
> I am currently fitting the numeric solutions of ODEs to data. I wrote an
> objective function, a gradient on that objective function, and passed both
> to scipy.optimize.minimize. This worked fine on small problems, but all the
> scipy minimize methods broke down when the number of parameters got large
> (~40). My past experience in Matlab suggests that this happens because some
> parameter directions are MUCH more tightly constrained than others.
>
> The scipy methods are easy-to-use, but I was wondering if there were any
> alternatives that people had found to work well when the problem got too
> hard for scipy. Here is a list of features that I probably need:
>
> * black-box optimizer
> * local optimizer
> * uses supplied analytic gradient
> * allows bounds on parameters
> * accepts a maximum step size
>

I like Emcee (https://emcee.readthedocs.io/en/stable/tutorials/line/) for
fitting ODEs to data.

I'm not sure I fully understand what you mean by using the gradient. If you
have the analytic gradient, I assume you also have the solution to the ODE,
or you can at least numerically integrate the gradient to get that
solution. So at that point there's no need to solve the ODE during the
fitting process?

Cheers,
Ralf



> _______________________________________________
> SciPy-User mailing list
> SciPy-User at python.org
> https://mail.python.org/mailman/listinfo/scipy-user
>
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From david at drhagen.com  Wed Mar 11 10:32:02 2020
From: david at drhagen.com (David Hagen)
Date: Wed, 11 Mar 2020 10:32:02 -0400
Subject: [SciPy-User] Optimization methods for fitting ODEs
In-Reply-To: <CABL7CQgL+oG7pQb5YWttij48KsmW2b9C-DCD2czNDCHAR-6ksw@mail.gmail.com>
References: <CALO6Ft5-6WoazbM1sYRxkb-h4DydFKSPU4j3de3gOZxXsSgtbQ@mail.gmail.com>
 <CABL7CQgL+oG7pQb5YWttij48KsmW2b9C-DCD2czNDCHAR-6ksw@mail.gmail.com>
Message-ID: <CALO6Ft5h7m=j+SGKUmWD-FoayFR+V3up6E+0VXgUem+CSpi46A@mail.gmail.com>

> I'm not sure I fully understand what you mean by using the gradient.

By analytic gradient, I mean as opposed to a finite difference gradient.
You are correct that I still have to numerically integrate the forward
sensitivities and then propagate the sensitivities via chain rule through
the the objective function.
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From charlesr.harris at gmail.com  Tue Mar 17 13:32:35 2020
From: charlesr.harris at gmail.com (Charles R Harris)
Date: Tue, 17 Mar 2020 11:32:35 -0600
Subject: [SciPy-User] NumPy 1.18.2 released.
Message-ID: <CAB6mnxJ_0mGqTz8LgW5M0cbrt+vZD3p9Lr0DCuqbp2H589rd1Q@mail.gmail.com>

Hi All,

On behalf of the NumPy team I am pleased to announce that NumPy 1.18.2 has
been released.This small release contains a fix for a performance
regression in numpy/random and several bug/maintenance updates.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and OpenBLAS
>= 3.7 to avoid errors on the Skylake architecture.  Wheels for this
release can be downloaded from PyPI <https://pypi.org/project/numpy/1.18.2>,
source archives and release notes are available from Github
<https://github.com/numpy/numpy/releases/tag/v1.18.2>.


*Contributors*

A total of 5 people contributed to this release.  People with a "+" by their
names contributed a patch for the first time.

   - Charles Harris
   - Ganesh Kathiresan +
   - Matti Picus
   - Sebastian Berg
   - przemb +


*Pull requests merged*

A total of 7 pull requests were merged for this release.

   - https://github.com/numpy/numpy/pull/15675: TST: move _no_tracing to
   testing._private
   - https://github.com/numpy/numpy/pull/15676: MAINT: Large overhead in
   some random functions
   - https://github.com/numpy/numpy/pull/15677: TST: Do not create gfortran
   link in azure Mac testing.
   - https://github.com/numpy/numpy/pull/15679: BUG: Added missing error
   check in ndarray.__contains__
   - https://github.com/numpy/numpy/pull/15722: MAINT: use list-based APIs
   to call subprocesses
   - https://github.com/numpy/numpy/pull/15729: REL: Prepare for 1.18.2
   release.
   - https://github.com/numpy/numpy/pull/15734: BUG: fix logic error when
   nm fails on 32-bit


Cheers,

Charles Harris
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From pierre.debuyl at kuleuven.be  Wed Mar 18 18:17:35 2020
From: pierre.debuyl at kuleuven.be (Pierre de Buyl)
Date: Wed, 18 Mar 2020 23:17:35 +0100
Subject: [SciPy-User] Cancellation of EuroSciPy 2020
Message-ID: <20200318221735.hjohcs2wzvxkt6qt@pierre-hp.fys.kuleuven.be>

Dear NumPy and SciPy communities,

It is with regret that we announce the cancellation of #euroscipy 2020 due to
the coronavirus outbreak, see https://euroscipy.org/2020/program.html

Stay safe, hasta pronto and remember social distancing and to wash your hands
for 20 seconds.

The EuroSciPy team.


From ralf.gommers at gmail.com  Wed Mar 18 18:33:39 2020
From: ralf.gommers at gmail.com (Ralf Gommers)
Date: Wed, 18 Mar 2020 23:33:39 +0100
Subject: [SciPy-User] Cancellation of EuroSciPy 2020
In-Reply-To: <20200318221735.hjohcs2wzvxkt6qt@pierre-hp.fys.kuleuven.be>
References: <20200318221735.hjohcs2wzvxkt6qt@pierre-hp.fys.kuleuven.be>
Message-ID: <CABL7CQhWCtHiVNXPkG9FDu8Js8UvUHWUNZaPKM_NN9vku0tYuw@mail.gmail.com>

On Wed, Mar 18, 2020 at 11:24 PM Pierre de Buyl <pierre.debuyl at kuleuven.be>
wrote:

> Dear NumPy and SciPy communities,
>
> It is with regret that we announce the cancellation of #euroscipy 2020 due
> to
> the coronavirus outbreak, see https://euroscipy.org/2020/program.html
>
> Stay safe, hasta pronto and remember social distancing and to wash your
> hands
> for 20 seconds.
>
> The EuroSciPy team.
>

Thanks for letting us know Pierre. Please wish the organizers all the best
- it must have been a hard decision after all the work you put in.

I was looking forward to EuroSciPy - next year!

Best,
Ralf
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From cimrman3 at ntc.zcu.cz  Tue Mar 31 13:26:49 2020
From: cimrman3 at ntc.zcu.cz (Robert Cimrman)
Date: Tue, 31 Mar 2020 19:26:49 +0200
Subject: [SciPy-User] ANN: SfePy 2020.1
Message-ID: <bf504528-9b38-acf9-b53c-95eba50a142e@ntc.zcu.cz>

I am pleased to announce release 2020.1 of SfePy.

Description
-----------

SfePy (simple finite elements in Python) is a software for solving systems of
coupled partial differential equations by the finite element method or by the
isogeometric analysis (limited support). It is distributed under the new BSD
license.

Home page: http://sfepy.org
Mailing list: https://mail.python.org/mm3/mailman3/lists/sfepy.python.org/
Git (source) repository, issue tracker: https://github.com/sfepy/sfepy

Highlights of this release
--------------------------

- reading/writing of additional mesh formats by using meshio [2]
- Python 3 only from now on

For full release notes see [1].

Cheers,
Robert Cimrman

[1] http://docs.sfepy.org/doc/release_notes.html#id1
[2] https://github.com/nschloe/meshio

---

Contributors to this release in alphabetical order:

Robert Cimrman
Lubos Kejzlar
Vladimir Lukes